With continuous development of computer technologies, an increasing amount of data is processed by a computer. A current big data era is also an era in which graph data prosperously develops. The graph data herein is data that has an association relationship with each other. On the basis of this, the computer usually needs to perform full big data analysis, and a large quantity of time resources and storage resources of the computer are consumed to obtain a precise search result.
To avoid resource consumption caused in a conventional search mechanism, a data sampling-based query (BlinkDB) technology is proposed in the other approaches. Original graph data is continuously sampled using a specific sampling algorithm, and a graph data sample is established and maintained in order to obtain a corresponding search result.
However, additional storage overheads need to be used to maintain the graph data sample in the BlinkDB technology in the other approaches. Consequently, storage resources of the computer are wasted to a great extent.